Cohen Shon, Schneidman-Duhovny Dina
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
Proteomics. 2023 Sep;23(17):e2200341. doi: 10.1002/pmic.202200341. Epub 2023 May 3.
Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.
大分子组装体在所有细胞过程中都起着重要作用。虽然最近基于深度学习的蛋白质结构预测取得了显著进展，但这些方法无法预测大型蛋白质复合物。整合结构建模方法通过对来自快速且可及的实验技术的数据进行计算整合来表征多亚基复合物。交联质谱法就是这样一种技术，它能提供关于交联残基邻近性的空间信息。解释交联数据集的挑战之一是设计一种评分函数，该函数在给定结构的情况下，能够量化其与数据的拟合程度。大多数方法设定交联残基的Cα原子之间距离的上限，并计算满足交联的比例。然而，交联剂跨越的距离在很大程度上取决于交联残基的邻域。在这里，我们设计了一个深度学习模型，用于根据交联残基对邻域的结构预测其最佳距离范围。我们发现，我们的模型对于蛋白质内和蛋白质间交联，分别能够以0.86和0.7的受试者工作特征曲线下面积预测距离范围。我们的深度评分函数可用于一系列结构建模应用。
